Sentiment_analysis_with_bert.py

!pip install -q -U watermark

!pip install -qq transformers


import transformers
from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup
import torch

import numpy as np
import pandas as pd
import seaborn as sns
from pylab import rcParams
import matplotlib.pyplot as plt
from matplotlib import rc
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report
from collections import defaultdict
from textwrap import wrap

from torch import nn, optim
from torch.utils.data import Dataset, DataLoader
import torch.nn.functional as F



sns.set(style='whitegrid', palette='muted', font_scale=1.2)

HAPPY_COLORS_PALETTE = ["#01BEFE", "#FFDD00", "#FF7D00", "#FF006D", "#ADFF02", "#8F00FF"]

sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))

rcParams['figure.figsize'] = 12, 8

RANDOM_SEED = 42
np.random.seed(RANDOM_SEED)
torch.manual_seed(RANDOM_SEED)

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


!gdown --id 1S6qMioqPJjyBLpLVz4gmRTnJHnjitnuV
!gdown --id 1zdmewp7ayS4js4VtrJEHzAheSW-5NBZv

df = pd.read_csv("reviews.csv")


sns.countplot(x='score', data = df)
plt.xlabel('review score');

def to_sentiment(rating):
  rating = int(rating)
  if rating <= 2:
    return 0
  elif rating == 3:
    return 1
  else:
    return 2

df['sentiment'] = df.score.apply(to_sentiment)

class_names = ['negative', 'neutral', 'positive']

print(df.sentiment)

ax = sns.countplot(x='sentiment', data = df)
plt.xlabel('review sentiment')
ax.set_xticklabels(class_names);

PRE_TRAINED_MODEL_NAME = 'bert-base-uncased'

tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)

sample_txt = 'When was I last outside? I am stuck at home for 2 weeks.'

tokens = tokenizer.tokenize(sample_txt)
token_ids = tokenizer.convert_tokens_to_ids(tokens)

print(f' Sentence: {sample_txt}')
print(f'   Tokens: {tokens}')
print(f'Token IDs: {token_ids}')

tokenizer.sep_token, tokenizer.sep_token_id

tokenizer.cls_token, tokenizer.cls_token_id

tokenizer.pad_token, tokenizer.pad_token_id

tokenizer.unk_token, tokenizer.unk_token_id

encoding = tokenizer.encode_plus(
  sample_txt,
  max_length=32,
  add_special_tokens=True, # Add '[CLS]' and '[SEP]'
  return_token_type_ids=False,
  pad_to_max_length=True,
  return_attention_mask=True,
  return_tensors='pt',  # Return PyTorch tensors
)

encoding.keys()

print(len(encoding['input_ids'][0]))
encoding['input_ids'][0]

print(len(encoding['attention_mask'][0]))
encoding['attention_mask']

tokenizer.convert_ids_to_tokens(encoding['input_ids'][0])

token_lens = []

for txt in df.content:
  tokens = tokenizer.encode(txt, max_length=512)
  token_lens.append(len(tokens))

sns.distplot(token_lens)
plt.xlim([0, 256]);
plt.xlabel('Token count');

MAX_LEN = 160

class GPReviewDataset(Dataset):

  def __init__(self, reviews, targets, tokenizer, max_len):
    self.reviews = reviews
    self.targets = targets
    self.tokenizer = tokenizer
    self.max_len = max_len

  def __len__(self):
    return len(self.reviews)

  def __getitem__(self, item):
    review = str(self.reviews[item])
    target = self.targets[item]

    encoding = self.tokenizer.encode_plus(
      review,
      add_special_tokens=True,
      max_length=self.max_len,
      return_token_type_ids=False,
      pad_to_max_length=True,
      return_attention_mask=True,
      return_tensors='pt',
    )

    return {
      'review_text': review,
      'input_ids': encoding['input_ids'].flatten(),
      'attention_mask': encoding['attention_mask'].flatten(),
      'targets': torch.tensor(target, dtype=torch.long)
    }

df_train, df_test = train_test_split(df, test_size=0.1, random_state=RANDOM_SEED)
df_val, df_test = train_test_split(df_test, test_size=0.5, random_state=RANDOM_SEED)

df_train.shape, df_val.shape, df_test.shape

def create_data_loader(df, tokenizer, max_len, batch_size):
  ds = GPReviewDataset(
    reviews=df.content.to_numpy(),
    targets=df.sentiment.to_numpy(),
    tokenizer=tokenizer,
    max_len=max_len
  )

  return DataLoader(
    ds,
    batch_size=batch_size,
    num_workers=4
  )

BATCH_SIZE = 16

train_data_loader = create_data_loader(df_train, tokenizer, MAX_LEN, BATCH_SIZE)
val_data_loader = create_data_loader(df_val, tokenizer, MAX_LEN, BATCH_SIZE)
test_data_loader = create_data_loader(df_test, tokenizer, MAX_LEN, BATCH_SIZE)

data = next(iter(train_data_loader))
data.keys()

print(data['input_ids'].shape)
print(data['attention_mask'].shape)
print(data['targets'].shape)

bert_model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)

last_hidden_state, pooled_output = bert_model(
  input_ids=encoding['input_ids'],
  attention_mask=encoding['attention_mask'],
  return_dict = False
)

last_hidden_state.shape

bert_model.config.hidden_size

pooled_output.shape

class SentimentClassifier(nn.Module):

  def __init__(self, n_classes):
    super(SentimentClassifier, self).__init__()
    self.bert = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)
    self.drop = nn.Dropout(p=0.3)
    self.out = nn.Linear(self.bert.config.hidden_size, n_classes)

  def forward(self, input_ids, attention_mask):
    returned = self.bert(
      input_ids=input_ids,
      attention_mask=attention_mask
    )
    pooled_output = returned["pooler_output"]
    output = self.drop(pooled_output)
    return self.out(output)

model = SentimentClassifier(len(class_names))
model = model.to(device)

input_ids = data['input_ids'].to(device)
attention_mask = data['attention_mask'].to(device)

print(input_ids.shape) # batch size x seq length
print(attention_mask.shape) # batch size x seq length

F.softmax(model(input_ids, attention_mask), dim=1)


EPOCHS = 6

optimizer = AdamW(model.parameters(), lr=2e-5, correct_bias=False)
total_steps = len(train_data_loader) * EPOCHS

scheduler = get_linear_schedule_with_warmup(
  optimizer,
  num_warmup_steps=0,
  num_training_steps=total_steps
)

loss_fn = nn.CrossEntropyLoss().to(device)

def train_epoch(
  model,
  data_loader,
  loss_fn,
  optimizer,
  device,
  scheduler,
  n_examples
):
  model = model.train()

  losses = []
  correct_predictions = 0

  for d in data_loader:
    input_ids = d["input_ids"].to(device)
    attention_mask = d["attention_mask"].to(device)
    targets = d["targets"].to(device)

    outputs = model(
      input_ids=input_ids,
      attention_mask=attention_mask
    )

    _, preds = torch.max(outputs, dim=1)
    loss = loss_fn(outputs, targets)

    correct_predictions += torch.sum(preds == targets)
    losses.append(loss.item())

    loss.backward()
    nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
    optimizer.step()
    scheduler.step()
    optimizer.zero_grad()

  return correct_predictions.double() / n_examples, np.mean(losses)

def eval_model(model, data_loader, loss_fn, device, n_examples):
  model = model.eval()

  losses = []
  correct_predictions = 0

  with torch.no_grad():
    for d in data_loader:
      input_ids = d["input_ids"].to(device)
      attention_mask = d["attention_mask"].to(device)
      targets = d["targets"].to(device)

      outputs = model(
        input_ids=input_ids,
        attention_mask=attention_mask
      )
      _, preds = torch.max(outputs, dim=1)

      loss = loss_fn(outputs, targets)

      correct_predictions += torch.sum(preds == targets)
      losses.append(loss.item())

  return correct_predictions.double() / n_examples, np.mean(losses)

%%time 
history = defaultdict(list)
best_accuracy = 0
 
for epoch in range(EPOCHS):
 
  print(f'Epoch {epoch + 1}/{EPOCHS}')
  print('-' * 10)

  train_acc, train_loss = train_epoch(
    model,
    train_data_loader,
    loss_fn,
    optimizer,
    device,
    scheduler,
    len(df_train)
  )
 
  print(f'Train loss {train_loss} accuracy {train_acc}')
 
  val_acc, val_loss = eval_model(
    model,
    val_data_loader,
    loss_fn,
    device,
    len(df_val)
  )
 
  print(f'Val   loss {val_loss} accuracy {val_acc}')
  print()
 
  history['train_acc'].append(train_acc)
  history['train_loss'].append(train_loss)
  history['val_acc'].append(val_acc)
  history['val_loss'].append(val_loss)

  if val_acc > best_accuracy:
    torch.save(model.state_dict(), 'best_model_state.bin')
    best_accuracy = val_acc

print(history['train_acc'])

list_of_train_accuracy= [t.cpu().numpy() for t in history['train_acc']]
list_of_train_accuracy

print(history['val_acc'])

list_of_val_accuracy= [t.cpu().numpy() for t in history['val_acc']]
list_of_val_accuracy

plt.plot(list_of_train_accuracy, label='train accuracy')
plt.plot(list_of_val_accuracy, label='validation accuracy')

plt.title('Training history')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend()
plt.ylim([0, 1]);

test_acc, _ = eval_model(
  model,
  test_data_loader,
  loss_fn,
  device,
  len(df_test)
)

print(('\n'))
print('Test Accuracy : ', test_acc.item())

def get_predictions(model, data_loader):
  model = model.eval()

  review_texts = []
  predictions = []
  prediction_probs = []
  real_values = []

  with torch.no_grad():
    for d in data_loader:

      texts = d["review_text"]
      input_ids = d["input_ids"].to(device)
      attention_mask = d["attention_mask"].to(device)
      targets = d["targets"].to(device)

      outputs = model(
        input_ids=input_ids,
        attention_mask=attention_mask
      )
      _, preds = torch.max(outputs, dim=1)

      probs = F.softmax(outputs, dim=1)

      review_texts.extend(texts)
      predictions.extend(preds)
      prediction_probs.extend(probs)
      real_values.extend(targets)

  predictions = torch.stack(predictions).cpu()
  prediction_probs = torch.stack(prediction_probs).cpu()
  real_values = torch.stack(real_values).cpu()
  return review_texts, predictions, prediction_probs, real_values

y_review_texts, y_pred, y_pred_probs, y_test = get_predictions(
  model,
  test_data_loader
)

print(classification_report(y_test, y_pred, target_names=class_names))

def show_confusion_matrix(confusion_matrix):
  hmap = sns.heatmap(confusion_matrix, annot=True, fmt="d", cmap="Blues")
  hmap.yaxis.set_ticklabels(hmap.yaxis.get_ticklabels(), rotation=0, ha='right')
  hmap.xaxis.set_ticklabels(hmap.xaxis.get_ticklabels(), rotation=30, ha='right')
  plt.ylabel('True sentiment')
  plt.xlabel('Predicted sentiment');

cm = confusion_matrix(y_test, y_pred)
df_cm = pd.DataFrame(cm, index=class_names, columns=class_names)
show_confusion_matrix(df_cm)

idx = 2

review_text = y_review_texts[idx]
true_sentiment = y_test[idx]
pred_df = pd.DataFrame({
  'class_names': class_names,
  'values': y_pred_probs[idx]
})

print("\n".join(wrap(review_text)))
print()
print(f'True sentiment: {class_names[true_sentiment]}')

sns.barplot(x='values', y='class_names', data=pred_df, orient='h')
plt.ylabel('sentiment')
plt.xlabel('probability')
plt.xlim([0, 1]);

review_text = input("Enter a comment for sentiment analysis: ")

encoded_review = tokenizer.encode_plus(
  review_text,
  max_length=MAX_LEN,
  add_special_tokens=True,
  return_token_type_ids=False,
  pad_to_max_length=True,
  return_attention_mask=True,
  return_tensors='pt',
)

input_ids = encoded_review['input_ids'].to(device)
attention_mask = encoded_review['attention_mask'].to(device)

output = model(input_ids, attention_mask)
_, prediction = torch.max(output, dim=1)

print(f'Review text: {review_text}')
print(f'Sentiment  : {class_names[prediction]}')

def suggest_improved_text(review_text, model, tokenizer):
    # Analyse du sentiment du texte d'origine
    sentiment = analyze_sentiment(review_text, model, tokenizer)

    # Si le sentiment est négatif ou neutre, générer une version améliorée plus positive
    if sentiment in ['negative', 'neutral']:
        # Prétraitement du texte
        encoded_input = tokenizer.encode_plus(
            review_text,
            max_length=MAX_LEN,
            add_special_tokens=True,
            return_token_type_ids=False,
            pad_to_max_length=True,
            return_attention_mask=True,
            return_tensors='pt'
        )

        input_ids = encoded_input['input_ids'].to(device)
        attention_mask = encoded_input['attention_mask'].to(device)
        outputs = model(input_ids, attention_mask)
        _, predicted_sentiment = torch.max(outputs, dim=1)

        improved_text = generate_improved_text(text, predicted_sentiment)

        return improved_text

    return review_text

def analyze_sentiment(review_text, model, tokenizer):
    encoded_input = tokenizer.encode_plus(
        review_text,
        max_length=MAX_LEN,
        add_special_tokens=True,
        return_token_type_ids=False,
        pad_to_max_length=True,
        return_attention_mask=True,
        return_tensors='pt'
    )

    input_ids = encoded_input['input_ids'].to(device)
    attention_mask = encoded_input['attention_mask'].to(device)
    outputs = model(input_ids, attention_mask)
    _, predicted_sentiment = torch.max(outputs, dim=1)

    return class_names[predicted_sentiment]
def generate_improved_text(review_text, predicted_sentiment):
    positive_words = ["marvellous", "fantastic", "excellent", "admirable", "formidable"]

    if predicted_sentiment == 0:
        improved_text = review_text + " " + " ".join(positive_words)
    else:
        improved_text = review_text

    return improved_text

from transformers import BertModel

bert_model = BertModel.from_pretrained('bert-base-uncased')

bert_model.save_pretrained('C:/Users/Marie-Ange/Downloads')

from transformers import pipeline


filename = "Sentiment_analysis_with_bert.py"
pipe = pipeline("sentiment-analysis", model="Group209/Sentiment_Analysis")
pipe("Sentiment_analysis_with_bert.py")